Oftentimes, users desire to adjust various aspects of an image to create a desired image. For example, a user may desire to modify contrast or exposure in an image. Typically, when multiple aspects are modified, such a manual process is time consuming as one aspect is modified and evaluated before a next aspect is modified and evaluated. This process can continue until a desired image is achieved.
Some conventional systems have been developed to automate image adjustments or corrections. Currently, automated image adjustments largely rely on large neural networks that require an extensive amount of training. Accordingly, such conventional systems necessitate an extensive amount of training images, a large amount of memory, and a slow processing time.